{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph statistics\n",
    "\n",
    "Here we visualize graph statistics for several real-world datasets and the synthetically generated dataset. We observe that the synthetic dataset covers a diverse spectrum of fundamental graph statistics in the real-world graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we compare clustering and shortest path length for all graph datasets. We find that the synthetic data (\"syn\") covers all regions covered by the cox2 and reddit-binary. Enzymes contains some more unique long and skinny graphs due to protein structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "600it [00:00, 1281.37it/s]\n",
      "467it [00:00, 1786.57it/s]\n",
      "2000it [00:16, 123.25it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys, os\n",
    "sys.path.insert(0, os.path.abspath(\"..\"))\n",
    "import networkx as nx\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from common import data, utils, combined_syn\n",
    "\n",
    "for dataset in [\"enzymes\", \"cox2\", \"reddit-binary\", \"syn\"]:\n",
    "    if dataset == \"syn\":\n",
    "        gen = combined_syn.get_generator([11])\n",
    "        graphs = [gen.generate() for i in range(10000)]\n",
    "    else:\n",
    "        train, test, task = data.load_dataset(dataset)\n",
    "        graphs = []\n",
    "        for i in range(10000):\n",
    "            graph, neigh = utils.sample_neigh(train, 11)\n",
    "            graphs.append(graph.subgraph(neigh))\n",
    "\n",
    "    clustering = [nx.average_clustering(G.subgraph(max(nx.connected_components(G), key=len))) for G in graphs]\n",
    "    path_length = [nx.average_shortest_path_length(G.subgraph(max(nx.connected_components(G), key=len))) for G in graphs]\n",
    "    plt.scatter(clustering, path_length, s=10, label=dataset)\n",
    "\n",
    "plt.xlabel(\"Clustering\")\n",
    "plt.ylabel(\"Shortest path length\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we compare diameters and densities of all graph types and again see that the synthetic data generally covers the real-world graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "600it [00:00, 1238.75it/s]\n",
      "467it [00:00, 1757.02it/s]\n",
      "2000it [00:15, 125.04it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dataset in [\"enzymes\", \"cox2\", \"reddit-binary\", \"syn\"]:\n",
    "    if dataset == \"syn\":\n",
    "        gen = combined_syn.get_generator([11])\n",
    "        graphs = [gen.generate() for i in range(10000)]\n",
    "    else:\n",
    "        train, test, task = data.load_dataset(dataset)\n",
    "        graphs = []\n",
    "        for i in range(10000):\n",
    "            graph, neigh = utils.sample_neigh(train, 11)\n",
    "            graphs.append(graph.subgraph(neigh))\n",
    "\n",
    "    diameter = [nx.diameter(G.subgraph(max(nx.connected_components(G), key=len))) for G in graphs]\n",
    "    density = [nx.density(G.subgraph(max(nx.connected_components(G), key=len))) for G in graphs]\n",
    "    plt.scatter(diameter, density, s=10, label=dataset)\n",
    "\n",
    "plt.xlabel(\"Diameter\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
